Recently, deep learning frameworks applied for image segmentation have shown promising results [1]}, [2]}. To segment distinct seabed textures in sonar images, a naive intuition is that we can use a similar network structure for seabed segmentation of SAS images because they have largely composted low-level textures. However, training a convolutional neural network (CNN) requires a considerable amount of labeled data to obtain good results. For SAS image segmentation, this means acquiring pixel-level annotations which is a costly process, and rarely do we have complete ground-truth from a diver survey. Consequently, humans often label regions which are easily recognized while forgoing areas difficult to distinguish such as region boundaries or blended regions. As a result, few pixel-level labeled datasets are available for training.
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